# Ollama vs LM Studio vs llama.cpp: which should you use?

> Compare Ollama, LM Studio and llama.cpp for running LLMs locally: setup, interface, API, performance and who each is best for, with a clear recommendation.

Source: https://devaiper.com/blog/ollama-vs-lm-studio-vs-llama-cpp
Published: 2026-10-08
Topics: Local LLMs, Ollama, LM Studio, llama.cpp

**Short answer:** Ollama is the easiest command-line and API option for developers, LM Studio is the friendliest desktop app with a local server, and llama.cpp is the lower-level engine for maximum control. Ollama and LM Studio build on the same idea: run GGUF models locally.

Three names come up whenever someone asks "how do I run an LLM on my own machine?" They are not exact rivals: one is an engine, the other two are friendly layers around it. Here is how to choose.

## How they relate

> **Diagram:** How the tools relate. llama.cpp is the inference engine at the base. Ollama and LM Studio are tools built on top of it, giving a command line and API for Ollama and a desktop app and local server for LM Studio. Your app talks to the local API on top.
> Your app or editor (calls a local HTTP API) → Ollama  |  LM Studio (model downloads, runner, CLI or GUI, local server) → llama.cpp (the inference engine that runs GGUF models) → Your hardware (CPU, NVIDIA / AMD GPU, Apple Silicon)
> Ollama and LM Studio make the engine easy to use. llama.cpp is the engine itself.

## At a glance

| | Ollama | LM Studio | llama.cpp |
|---|---|---|---|
| **What it is** | CLI + local API | Desktop app + local server | Inference engine and server |
| **Best for** | Developers, scripts, apps | Exploring models, chat, non-CLI users | Control, edge devices, custom builds |
| **Setup** | Installer, then one command | Installer, point and click | Build or download binaries |
| **Model discovery** | `ollama pull <model>` | Built-in model browser | Download GGUF files yourself |
| **API** | REST on localhost:11434 | Local server, OpenAI-compatible | `llama-server`, OpenAI-compatible |
| **Tuning knobs** | Modelfile, a few options | GUI sliders | Everything, via flags |
| **Learning curve** | Low | Lowest | Highest |

## Ollama

You install it, pull a model and run it:

```bash
ollama pull <model>
ollama run <model>
```

It also runs a local server. Hit it from code:

```ts
const res = await fetch("http://localhost:11434/api/chat", {
  method: "POST",
  body: JSON.stringify({
    model: "<model>",
    stream: false,
    messages: [{ role: "user", content: "Explain closures in one paragraph." }],
  }),
});
const data = await res.json();
console.log(data.message.content);
```

Pros: simplest developer workflow, scriptable, large library of models. Cons: fewer low-level knobs than raw llama.cpp.

## LM Studio

A desktop app. You search for a model, download it, and chat. It can also start a local server so your editor or app can use it. It is the easiest way to **try** models and compare them without touching a terminal. Cons: GUI-first, less natural for automation and servers.

## llama.cpp

The engine under the others. You choose the model file, the quantization, the number of layers on GPU, the context size, the threads. It runs on almost anything: NVIDIA, AMD, Apple Silicon, plain CPUs.

```bash
llama-server -m ./model-Q4_K_M.gguf -c 8192
```

Pros: maximum control and portability, great for edge devices. Cons: you manage files and flags yourself.

## Which one should you pick?

- **"I want to build something with a local model."** Ollama.
- **"I want to try many models and chat with them."** LM Studio.
- **"I need to run on a Raspberry Pi, tune every flag, or embed it."** llama.cpp.
- **"I need many users hitting one model at high throughput."** Look at a serving engine such as vLLM, which is a different class of tool.

You can use more than one. They all read the same kind of GGUF file.

## Before you start

New to this? Follow [how to run an LLM locally](https://devaiper.com/blog/how-to-run-llm-locally) first.

1. Estimate memory first: [how much RAM do you need](https://devaiper.com/blog/how-much-ram-to-run-an-llm-locally).
2. Pick a [quantization](https://devaiper.com/blog/what-is-llm-quantization), usually 4-bit.
3. Test the model on your own cases with a small [eval](https://devaiper.com/blog/llm-evals-explained). A local model that is fine for classification may not be fine for complex coding.

## FAQ

### How do I run an LLM locally?

Check that the model fits in your memory, install a runner such as Ollama, pull a model and run it. The step-by-step guide is in the post How to run an LLM locally.

### What is the difference between Ollama, LM Studio and llama.cpp?

llama.cpp is the inference engine. Ollama wraps a model runner with a simple CLI and local API. LM Studio is a desktop app with a model browser, chat UI and a local server.

### Which is best for developers?

Ollama is the usual pick for developers because it is scriptable, has a REST API and is easy to use in code. LM Studio is better if you prefer a GUI. llama.cpp is for when you need full control.

### Can I use a local model with code written for a cloud API?

Often yes. Ollama, LM Studio and llama.cpp's server expose OpenAI-compatible endpoints, so many clients work by changing the base URL. Check which features, such as tool calling, your chosen model supports.

### Do these tools send my data to the cloud?

Running a downloaded model through these tools happens on your machine, which is the main privacy advantage of local LLMs. Downloading models and checking for updates does use the network.